Monthly Archives: December 2010

Where does the money go?

A regular Mule reader drew my attention to an article in the Sydney Morning Herald (also published in The Age) which attempts to defend Australian banks from some of the criticisms levelled at them in recent months. It is something of a laundry list of points, some accurate, some dubious and has little in the way of hard data behind it.

What my correspondent was more interested in, however, was that one powerful argument was missing. If banks had not bolstered their margins by raising mortgage rates by more than the Reserve Bank cash rate rises, the Reserve Bank would in all likelihood have increased the cash rate by even more. This contention is supported by the Reserve Bank’s own board meeting minutes from the 2 November meeting. Discussing the considerations which led to the November rate hike, the following observations appear:

Members noted that lending rates might increase by more than the cash rate, but this tendency would not be lessened by delaying a change in the cash rate. Lending rates had been rising relative to the cash rate since the global financial crisis, and the Board had taken this into account in setting the cash rate. It would continue to take account of any changes in margins in its decisions in the period ahead.

From this it seems clear that if the banks had kept to moving their mortgage rates in line with the cash rate, the cash rate would now be higher and the end results for borrowers would be much the same.

Of course, if this had happened, bank margins would have been squeezed, which leads to this question from my correspondent:

Where banks don’t increase margins but RBA increases base rate more so overall level the same, where does the “banks’ profit” go? RBA [Reserve Bank of Australia]?

This question gets to the heart of how banks work.

While we tend to think of banks as lenders, it can be more useful to think of them as intermediaries between borrowers and lenders. The real lenders are the banks’ depositors and bondholders. Banks pay interest on deposits and bonds and charge a somewhat higher rate interest on their loans. The difference between the interest they pay and the interest they receive is their net interest margin which, along with fees and charges, is their source of profit. In the wake of the financial crisis, the market for deposits has become very competitive and bond investors now demand higher returns on bank debt compared to lower risk alternatives (such as government bonds…at least if the government in question is not European!). Both of these effects have resulted in the interest banks pay increasing by more than the amount the Reserve Bank’s cash rate has increased. Banks have attempted to recoup the resulting increases in the interest they pay by passing through bigger increases to their borrowers (you can read more of the details in an earlier post on bank funding costs).

So, if banks had kept their mortgage rates strictly in line with the Reserve Bank’s cash rate, their margins would certainly have been smaller than they are today. If that had happened, where would the money have done? It does not go to the Reserve Bank: while they set the target rate, the Reserve Bank itself does very little lending at that rate. Rather they ensure that any lending overnight from one bank to another is done at or very close to the target rate by promising to lend or borrow large amounts at rates only slightly above or below the target respectively. No, the real beneficiaries of the higher rates are the ultimate lenders: depositors and bondholders.

Anyone with a balance in a superannuation fund is likely to have a certain amount invested in bond funds which would invest in, among other things, bonds issued by banks. Self-funded retirees and others seeking to keep their investment risk to a minimum may have money in bank term deposits rather than shares or property. All of these people lend money to banks and benefit through higher earnings when interest rates go up*. The banks do get some of the benefit themselves. Some deposit balances are paid little or no interest and so when the cash rate rises, these deposits represent an increasingly cheap source of funds for banks, although these low interest balances represent a much smaller proportion of banks’ funding than they used to.

The effect of changing interest rates is thus an exercise in wealth redistribution between the ultimate borrowers (including those borrowing to buy a home), the ultimate lenders (depositors and investors) and the banks themselves. What we have seen over recent months can be seen as a bit of a tussle between banks on the one hand and depositors and investors on the other as to who should get how much of the higher rates borrowers are paying.

* There is a timing issue for bond investors: fixed rate bonds actually fall in value when interest rates go up, but from that point onwards the ongoing earnings of the investment are higher.

Micromorts

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Everyone knows hang-gliding is risky. How could throwing yourself off a mountain not be? But then again, driving across town is risky too. In both cases, the risks are in fact very low and assessing and comparing small risks is tricky.

Ronald A. Howard, the pioneer of the field of decision analysis (not the Happy Days star turned director) put it this way:

A problem we continually face in describing risks is how to discuss small probabilities. It appears that many people consider probabilities less than 1 in 100 to be “too small to worry about.” Yet many of life’s serious risks, and medical risks in particular, often fall into this range.

R. A. Howard (1989)

Howard’s solution was to come up with a better scale than percentages to measure small risks. Shopping for coffee you would not ask for 0.00025 tons (unless you were naturally irritating), you would ask for 250 grams. In the same way, talking about a 1/125,000 or 0.000008 risk of death associated with a hang-gliding flight is rather awkward. With that in mind. Howard coined the term “microprobability” (μp) to refer to an event with a chance of 1 in 1 million and a 1 in 1 million chance of death he calls a “micromort” (μmt). We can now describe the risk of hang-gliding as 8 micromorts and you would have to drive around 3,000km in a car before accumulating a risk of 8μmt, which helps compare these two remote risks.

Before going too far with micromorts, it is worth getting a sense of just how small the probabilities involved really are. Howard observes that the chance of flipping a coin 20 times and getting 20 heads in a row is around 1μp and the chance of being dealt a royal flush in poker is about 1.5μp. In a post about visualising risk I wrote about “risk characterisation theatres” or, for more remote risks, a “risk characterisation stadium”. The lonely little spot in this stadium of 10,000 seats represents a risk of 100μp.

One enthusiastic user of the micromort for comparing remote risks is Professor David Spiegelhalter, a British statistician who holds the professorship of the “Public Understanding of Risk” at the University of Cambridge. He recently gave a public lecture on quantifying uncertainty at the London School of Economics*. The chart below provides a micromort comparison adapted from some of the mortality statistics appearing in Spiegelhalter’s lecture. They are UK figures and some would certainly vary from country to country.

Based on these figures, a car trip across town comes in at a mere 0.003μmt (or perhaps 3 “nanomorts”) and so is much less risk, if less fun, than a hang-gliding flight.

It is worth noting that assessing the risk of different modes of travel can be controversial. It is important to be very clear whether comparisons are being made based on risk per annum, risk per unit distance or risk per trip. These different approaches will result in very different figures. For example, for most people plane trips are relatively infrequent (which will make annual risks look better), but the distances travelled are much greater (so the per unit distance risk will look much better than the per trip risk).

Here are two final statistics to round out the context for the micromort unit of measurement: the average risk of premature death (i.e. dying of non-natural causes) in a single day for someone living in a developed nation is about 1μmt and the risk for a British soldier serving in Afghanistan for one day is about 33μmt.

*Thanks to Stephen from the SURF group for bringing this lecture to my attention.

The Chinese growth engine

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As Australia’s economic fortunes continue to surpass the likes of the US, UK and Europe, it is hard to escape a lingering nervousness about what could happen if the mining boom were to collapse. What if the Chinese juggernaut were to falter? Would we be doomed?

Having a conversation exactly like this earlier in the week, I was reminded of a post I wrote more than a year ago which showed surprisingly (to me at least) that exports to China were contributing only 3% to Australia’s gross domestic product (GDP). In yesterday’s Sydney Morning Herald, economist Ross Gittins tried to bring some perspective to the nervous by pointing out that 80% of Australia’s economic activity is domestic and concluded that:

Take away mining and we wouldn’t be quite as rich as we are, but most of the economy would look much the same as it does. Most of us would still have good, secure, well-paid jobs.

Of course, not everyone is taking such an encouraging line. Over on the Mule Stable, one econo-pessimist drew my attention to this interview with hedge fund manager John Chanos, who has been predicting a bursting of the Chinese economic bubble for some time now. As well as showing a very detailed knowledge of China’s construction industry, Chanos notes that were China’s economy to stall, the US would be much better positioned to cope with it than countries like, say, Australia. That was supposed to be good news for American viewers…not so cheering for those of us on this side of the globe!

All of this suggested that an update of the trade statistics was overdue. The results are as one might expect: the contribution that exports to China make to Australia’s GDP has risen from the 3% I noted in August 2009 to almost 4% as at September 2010.

GDP from Exports to China (Dec-1988 to Sep-2010)

So, while 4% may still be small compared to the 80% of activity that is generated internally in Australia, the real story here is growth, as the steepness of the chart dramatically illustrates. That increase in exports has contributed almost 1% to Australia’s GDP growth for the year! Here is the rolling annual change in the contribution that exports to China make to Australia’s GDP.

Annual Change in GDP from Exports to China (Dec-1988 to Sep-2010)

Not wishing to forget Gittins’ point that we should consider total contributions to the economy, not just exports, it is hard to resist wondering how many of our exports now go to China. The answer is: a lot and growing.

China’s Share of Exports (Dec-1988 to Sep-2010)

So, where does that leave us? Gittins is not wrong, and a collapse in the Chinese economy would not suddenly put everyone in Australia out of work. Nevertheless, it would certainly take a lot of the wind out of our economic sails. Furthermore, given the amount of attention China and the mining industry have in our national consciousness at the moment, it is worth recalling the words of that sage John Maynard Keynes:

Even apart from the instability due to speculation, there is the instability due to the characteristic of human nature that a large proportion of our positive activities depend on spontaneous optimism rather than mathematical expectations, whether moral or hedonistic or economic. Most, probably, of our decisions to do something positive, the full consequences of which will be drawn out over many days to come, can only be taken as the result of animal spirits – a spontaneous urge to action rather than inaction, and not as the outcome of a weighted average of quantitative benefits multiplied by quantitative probabilities.

There is little doubt that if Chanos is right about China, our animal spirits would not take it too well.

Data source: based on Australian Bureau of Statistics (copyright Creative Commons Attribution). Note that all export figures here represent exports of merchandise, so exclude services.

Job guarantee on “Mule Bites”

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It’s official! The Mule Bites podcast has been launched.

Regular readers will know that I travelled to Newcastle at the beginning of the month for the 12th annual CofFEE conference. Conference organiser and director of CofFEE, Professor Bill Mitchell, was kind enough to allow me to interview him after the conference. Fortunately, a couple of failed attempts to get the recorder to work did not exhaust Bill’s patience and I ended up with about half an hour of audio covering both Bill’s idea of a “job guarantee” to achieve full employment and a discussion of the nature of money. The workings of fiat money is a subject I have discussed a number of times here on the blog, so I thought that the job guarantee would make a good first podcast topic.

For those not satisfied with the 16 minutes in this podcast, I am planning another episode with the money discussion and will also make the full, unedited interview available.

Audio credits: Mule Bites theme by ToastCorp, train sounds CC by Robinhood76.

UPDATE: there were some balance problems in the audio mix, which have now been improved. Thanks for the feedback, keep it coming! I am well on my way to learning basic audio engineering.

Polls apart on climate change

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Regular Stubborn Mule guest James Glover (@zebra) turns his statistical expertise on some apparently contradictory polls gauging opinions on climate change.

Two polls came out today on the question of whether people believed climate change is real and if so whether it is caused by human activity. The first was a Newspoll published in The Australian and the second was by Essential Media and was commented upon by Essential’s Peter Lewis on ABC’s The Drum. Intriguingly, the Newspoll suggests 73% of Australians believe in climate change with a significant human contribution (so called Anthropogenic Global Warming or AGW). Now The Australian has copped a bit of flak lately for its alleged anti-climate change agenda, but leaving that aside this poll suggests that AGW should be practically a closed book politically as an overwhelming majority believe in it. Essential Media describes themselves as a research tool for progressive political campaigns. Essential’s poll indicates that only 45% of people believe in climate change caused by human activity. In the accepted narrative of such things the results would have been around the other way and the tweetsphere would be apoplectic accusing The Australian and News Corp of once again distorting Newspoll results for their own right-wing political agenda. So what is happening here?

First a note on sampling error. Essential polled 1896 people while Newspoll contacted 1,123 people. For polls where the expected split is approximately 50% a good rule of thumb for margin of error (MoE) is 1/√sample size. In the two polls here this gives MoEs of 2.2% and 3.0% respectively. MoE represents two standard deviations from the sample average so differences of 25% are extremely unlikely (like 10^-10 probability unlikely) to be explained by a unfortunate random choice of sample from the general population.

The most likely explanation is that one or both of these polls suffer from an underlying sampling bias. This would be easy enough to generate artificially—just poll people in Newtown if you want to get more people who believe in AGW or in Bob Katter’s seat for the opposite result (is this a little glib? Maybe, but you know I am right). But legitimate pollsters like Essential and Newspoll rely on the rigour of their sampling technique. Especially as every time you publish a controversial result, a large section of the population who disagree with it will accuse you of bias. There are a number of techniques to reduce bias—one is to ask coquestions whose population statistics are well accepted. For example if in your poll you found that 46% of the respondents were female and 54% were male you can readjust the result to reflect the actual population average of 51:49. I assume both polling organisations follow standard methodologies to minimise bias. Often though their actual methodologies are proprietary so question marks remain. A famous political polling agency was well know to always come up with polling results that reflected the political opinions of its founder after “adjustment for bias”.

Some indication that there isn’t an overwhelming bias are some additional questions about voting intention. Here are the results:

	Essential	Newspoll
Coalition	45%	41%
Labor	38%	34%
Green	11%	14%
Other	6%	11%

I would have to say that the differences in the numbers are on the borderline of being consistent with the MoEs I estimated. In any event the Newspoll which has a higher number believing in AGW has less Coalition voters (though about the same Labor+Green votes). It seems unlikely that the votes for Independents and other alone could account for the 28% difference in the polls on the question of AGW.

So that leaves us with the polls themselves. I have assumed so far that they asked the same questions, but there are major differences. Here are the actual questions and results:

Essential

Climate change is happening and is caused by human activity	45%
We are just witnessing a normal fluctuation in the Earth’s climate	36%
Don’t know	19%

Newspoll

No climate change	18%
Climate change solely caused by human activity	18%
Climate change partly caused by human activity	55%
Climate change not caused by human activity	3%
Believe in climate change but don’t know cause	2%
Don’t know if climate change is real	5%

Now what appears at first to be a headline difference between the polls is more subtle. It is quite hard (I tried) to map the answers between them exactly to compare the results. For example Essential doesn’t ask if the respondents directly if they don’t believe in climate change at all (18% in Newspoll) so presumably the climate skeptics get lumped under “Don’t know” (19%) which will also include those who believe in climate change but don’t know if it is caused by human activity or don’t know if climate change is real. That Newspoll total of “don’t knows” and skeptics is 23%, a bit higher than Essentials “Don’t know” of 19% but within the MoEs as reflected by the voting intention results.

However we can try to compare the two main results which boil down to “Believe climate change is real and human activity is significantly affecting it” of 73% (Newspoll’s headline result combining “solely” and “partly” caused by human activity) vs Essential’s “Climate change is happening and is caused by human activity” of 45%. The difference appears huge. The only thing I can think to explain this is that when not offered the choice of “solely” vs “partly” caused by human activity the Essential respondents threw their lot in with “caused by a normal fluctuation in the Earth’s climate”. In other words the results are consistent if most people who believe that “climate change is real” but don’t believe it is “solely caused by human activity” believe it is “partly caused by human activity” but mostly due to “natural fluctuations in the Earth’s climate”.

What is clear here is that the wording of polls is important and that both polls failed to tease out the subtle distinctions in people’s views on climate change (though Newspoll did a better job of this than Essential). There are also question marks about the sampling bias as shown in the voting intention results. But the headlines of both polls will superficially look like totally different results. And that is a problem when the results are used to support political rather than scientific views on anthropogenic global warming as fact or fiction.

Coffee day 2

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The CofFEE conference came to a close on Friday. The morning started with some mathematics as Trond Andresen (visiting from Norway) talked us through a simple model of the impact of the Basel capital adequacy rules on money supply and debt. He concluded that an unintended side-effect of the rules was to condemn our economies to exponential growth in private sector debt. I have some suspicions that the model is a bit too simple, but I will think about it some more and may post a discussion about it in the future.

Another interesting presentation gave a sneak-preview of a new website developed by Bill Mitchell and his colleagues at CofFEE. The website allows you to explore Australian labour market statistics by “functional regions” rather than the traditional regions used by the href=”http://www.abs.gov.au”>Australian Bureau of Statistics (ABS). The ABS regions have traditionally been based on administrative regions (post code, council areas, etc) which do not necessarily cluster areas of similar demographic characteristics. Bill has used spatial statistical techniques to come up with more useful regional definitions. Interestingly, the ABS have followed the work closely and have in fact adapted their own regions as a result, although they remain somewhat constrained by the need to have large enough populations in each region for statistically significant results. The new functional regions website features some impressive data visualisation, including integration with Google maps. I will post a link as soon as the site is public.

Visiting US academic Randall Wray closed the session with a discussion of the shortcomings of the US Federal Reserve’s latest efforts to stimulate the ailing US economy. “Quantitative easing” (known as QE2 as it’s the second time the have used this tool) has been controversial across the political spectrum and has raised the hackles of some countries who see it as an effort to devalue the US dollar as part of a “currency war”. Wray argued that QE2 was not like a “helicopter drop of money” as some critics fashion it, nor would it have a lasting effect on the economy. Nevertheless, he was also critical of the program, but for a different reason, simply that it would do nothing to stimulate the economy. The talk also had a polemical take on the undemocratic nature of the Federal Reserve which, at least until recently, has been far less transparent than the Treasury.

Before jumping on the train back to Sydney, I was lucky enough to record an interview with Bill Mitchell in which he described his primary policy prescription for dealing with unemployment: the Job Guarantee. As soon as I have edited and polished the recording, I will be posting it here on the blog. It will be the first in what I hope to be a series of audio “Mule Bites”. Yes, stay tuned for a Stubborn Mule podcast!

Stubborn Mule

Obstinately objective